Strategic Optimization of Gig Economy Integration in Healthcare Workforce Management

Staffing shortages, inefficient resource allocation, and inflexible scheduling systems are among workforce challenges across different healthcare systems. To address these pressing issues, the current study presents a novel optimization model that integrates gig workers with traditional full-time staff. The developed model is a multi-objective mathematical model that simultaneously minimises labour costs, maximises workforce coverage, and reduces scheduling disruptions while adhering to operational constraints. Data was collected from a healthcare system in the US. Based on the data collected, this study conducted a comparative analysis of metaheuristic algorithms. The results obtained showed that the genetic algorithm (GA) outperforms simulated annealing and differential evolution. GA achieved a 16.7% reduction in penalty costs and an optimal total objective value of $1,010,800. Using GA as a solution method for the developed model, the results obtained showed that a balanced workforce composition (51.5% gig workers at $200/hour and 48.5% full-time staff at $250/hour) yields a cost-efficient average of $224.27/hour. The analysis of role-specific results revealed that physicians achieved 94% shift coverage satisfaction, while support staff exhibited higher variability (25% understaffed shifts). The model also identifies critical gaps, for instance, there was a 33% uncovered physician shifts during peak afternoon demand. This study contributes a data-driven, scalable solution for healthcare administrators to harness the gig economy's flexibility while mitigating its ethical and operational risks. Hence, the developed model offers a transformative approach to workforce planning in resource-constrained environments.